SFT-SGAT: A semi-supervised fine-tuning self-supervised graph attention network for emotion recognition and consciousness detection

• Published in: Neural networks Vol. 180; p. 106643
• Main Authors: Qiu, Lina, Zhong, Liangquan, Li, Jianping, Feng, Weisen, Zhou, Chengju, Pan, Jiahui
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.12.2024
• Subjects: Adult; Attention - physiology; Brain-Computer Interfaces; Consciousness - physiology; Cross-subject; Electroencephalography - methods; Emotion recognition; Emotions - physiology; Female; Graph attention network; Humans; Male; Neural Networks, Computer; Self-supervised; Semi-supervised; Supervised Machine Learning; Emotion recognition; Self-supervised; Cross-subject; Graph attention network; Semi-supervised

Emotional recognition is highly important in the field of brain-computer interfaces (BCIs). However, due to the individual variability in electroencephalogram (EEG) signals and the challenges in obtaining accurate emotional labels, traditional methods have shown poor performance in cross-subject emo...